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Médias (91)

Head down (wav version)

26 septembre 2011, par kent1

Mis à jour : Avril 2013

Langue : English

Type : Audio

Tags : audio, Nine Inch Nails, Musique, wav

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Echoplex (wav version)

26 septembre 2011, par kent1

Mis à jour : Avril 2013

Langue : English

Type : Audio

Tags : audio, Nine Inch Nails, Musique, wav

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Discipline (wav version)

26 septembre 2011, par kent1

Mis à jour : Avril 2013

Langue : English

Type : Audio

Tags : audio, Nine Inch Nails, Musique, wav

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Letting you (wav version)

26 septembre 2011, par kent1

Mis à jour : Avril 2013

Langue : English

Type : Audio

Tags : audio, Nine Inch Nails, Musique, wav

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1 000 000 (wav version)

26 septembre 2011, par kent1

Mis à jour : Avril 2013

Langue : English

Type : Audio

Tags : audio, wave, Nine Inch Nails, Musique

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999 999 (wav version)

26 septembre 2011, par kent1

Mis à jour : Avril 2013

Langue : English

Type : Audio

Tags : audio, Nine Inch Nails, Musique, wav

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Autres articles (13)

Ajouter notes et légendes aux images

7 février 2011, par kent1

Pour pouvoir ajouter notes et légendes aux images, la première étape est d’installer le plugin "Légendes".
Une fois le plugin activé, vous pouvez le configurer dans l’espace de configuration afin de modifier les droits de création / modification et de suppression des notes. Par défaut seuls les administrateurs du site peuvent ajouter des notes aux images.
Modification lors de l’ajout d’un média
Lors de l’ajout d’un média de type "image" un nouveau bouton apparait au dessus de la prévisualisation (...)
Organiser par catégorie

17 mai 2013, par etalarma

Dans MédiaSPIP, une rubrique a 2 noms : catégorie et rubrique.
Les différents documents stockés dans MédiaSPIP peuvent être rangés dans différentes catégories. On peut créer une catégorie en cliquant sur "publier une catégorie" dans le menu publier en haut à droite ( après authentification ). Une catégorie peut être rangée dans une autre catégorie aussi ce qui fait qu’on peut construire une arborescence de catégories.
Lors de la publication prochaine d’un document, la nouvelle catégorie créée sera proposée (...)
Les thèmes de MediaSpip

4 juin 2013

3 thèmes sont proposés à l’origine par MédiaSPIP. L’utilisateur MédiaSPIP peut rajouter des thèmes selon ses besoins.
Thèmes MediaSPIP
3 thèmes ont été développés au départ pour MediaSPIP : * SPIPeo : thème par défaut de MédiaSPIP. Il met en avant la présentation du site et les documents média les plus récents ( le type de tri peut être modifié - titre, popularité, date) . * Arscenic : il s’agit du thème utilisé sur le site officiel du projet, constitué notamment d’un bandeau rouge en début de page. La structure (...)

1 | 2 | 3 | 4 | 5

Sur d’autres sites (2476)

avcodec/x86/vvc/vvc_alf : add alf classify avx2 optimizations

13 mai 2024, par Wu Jianhua

avcodec/x86/vvc/vvc_alf : add alf classify avx2 optimizations

vvc_alf_classify_4x4_8_c : 480.5
vvc_alf_classify_4x4_8_avx2 : 203.0
vvc_alf_classify_4x4_10_c : 439.0
vvc_alf_classify_4x4_10_avx2 : 171.7
vvc_alf_classify_4x8_8_c : 690.0
vvc_alf_classify_4x8_8_avx2 : 267.0
vvc_alf_classify_4x8_10_c : 706.5
vvc_alf_classify_4x8_10_avx2 : 215.7
vvc_alf_classify_4x12_8_c : 935.7
vvc_alf_classify_4x12_8_avx2 : 377.2
vvc_alf_classify_4x12_10_c : 937.2
vvc_alf_classify_4x12_10_avx2 : 330.0
vvc_alf_classify_4x16_8_c : 1216.5
vvc_alf_classify_4x16_8_avx2 : 439.7
vvc_alf_classify_4x16_10_c : 1197.5
vvc_alf_classify_4x16_10_avx2 : 387.0
vvc_alf_classify_4x20_8_c : 1431.0
vvc_alf_classify_4x20_8_avx2 : 556.7
vvc_alf_classify_4x20_10_c : 1401.2
vvc_alf_classify_4x20_10_avx2 : 472.5
vvc_alf_classify_4x24_8_c : 1650.5
vvc_alf_classify_4x24_8_avx2 : 615.5
vvc_alf_classify_4x24_10_c : 1737.7
vvc_alf_classify_4x24_10_avx2 : 534.7
vvc_alf_classify_4x28_8_c : 1879.2
vvc_alf_classify_4x28_8_avx2 : 743.5
vvc_alf_classify_4x28_10_c : 1942.5
vvc_alf_classify_4x28_10_avx2 : 622.2
vvc_alf_classify_4x32_8_c : 2119.0
vvc_alf_classify_4x32_8_avx2 : 890.5
vvc_alf_classify_4x32_10_c : 2139.7
vvc_alf_classify_4x32_10_avx2 : 671.2
vvc_alf_classify_4x36_8_c : 2359.5
vvc_alf_classify_4x36_8_avx2 : 915.7
vvc_alf_classify_4x36_10_c : 2388.5
vvc_alf_classify_4x36_10_avx2 : 774.2
vvc_alf_classify_4x40_8_c : 2601.5
vvc_alf_classify_4x40_8_avx2 : 973.7
vvc_alf_classify_4x40_10_c : 2623.2
vvc_alf_classify_4x40_10_avx2 : 827.0
vvc_alf_classify_4x44_8_c : 2915.5
vvc_alf_classify_4x44_8_avx2 : 1092.2
vvc_alf_classify_4x44_10_c : 2859.2
vvc_alf_classify_4x44_10_avx2 : 924.0
vvc_alf_classify_4x48_8_c : 3260.7
vvc_alf_classify_4x48_8_avx2 : 1157.5
vvc_alf_classify_4x48_10_c : 3225.2
vvc_alf_classify_4x48_10_avx2 : 1326.7
vvc_alf_classify_4x52_8_c : 3332.2
vvc_alf_classify_4x52_8_avx2 : 1267.2
vvc_alf_classify_4x52_10_c : 3385.7
vvc_alf_classify_4x52_10_avx2 : 1075.0
vvc_alf_classify_4x56_8_c : 3591.2
vvc_alf_classify_4x56_8_avx2 : 1330.5
vvc_alf_classify_4x56_10_c : 3636.0
vvc_alf_classify_4x56_10_avx2 : 1198.2
vvc_alf_classify_4x60_8_c : 3944.2
vvc_alf_classify_4x60_8_avx2 : 1453.2
vvc_alf_classify_4x60_10_c : 3858.5
vvc_alf_classify_4x60_10_avx2 : 1276.0
vvc_alf_classify_4x64_8_c : 4062.0
vvc_alf_classify_4x64_8_avx2 : 1509.2
vvc_alf_classify_4x64_10_c : 4095.5
vvc_alf_classify_4x64_10_avx2 : 1321.5
vvc_alf_classify_4x68_8_c : 4323.2
vvc_alf_classify_4x68_8_avx2 : 1624.0
vvc_alf_classify_4x68_10_c : 4357.7
vvc_alf_classify_4x68_10_avx2 : 1422.0
vvc_alf_classify_4x72_8_c : 4555.0
vvc_alf_classify_4x72_8_avx2 : 1693.0
vvc_alf_classify_4x72_10_c : 8527.7
vvc_alf_classify_4x72_10_avx2 : 1465.2
vvc_alf_classify_4x76_8_c : 13716.2
vvc_alf_classify_4x76_8_avx2 : 1858.7
vvc_alf_classify_4x76_10_c : 4832.0
vvc_alf_classify_4x76_10_avx2 : 1575.5
vvc_alf_classify_4x80_8_c : 5030.0
vvc_alf_classify_4x80_8_avx2 : 1869.0
vvc_alf_classify_4x80_10_c : 5097.7
vvc_alf_classify_4x80_10_avx2 : 1620.0
vvc_alf_classify_4x84_8_c : 5273.5
vvc_alf_classify_4x84_8_avx2 : 2048.2
vvc_alf_classify_4x84_10_c : 5301.7
vvc_alf_classify_4x84_10_avx2 : 1787.7
vvc_alf_classify_4x88_8_c : 5522.2
vvc_alf_classify_4x88_8_avx2 : 2118.2
vvc_alf_classify_4x88_10_c : 5568.5
vvc_alf_classify_4x88_10_avx2 : 1822.7
vvc_alf_classify_4x92_8_c : 5768.7
vvc_alf_classify_4x92_8_avx2 : 2230.0
vvc_alf_classify_4x92_10_c : 5964.2
vvc_alf_classify_4x92_10_avx2 : 1929.7
vvc_alf_classify_4x96_8_c : 6020.5
vvc_alf_classify_4x96_8_avx2 : 2291.0
vvc_alf_classify_4x96_10_c : 6758.5
vvc_alf_classify_4x96_10_avx2 : 1979.7
vvc_alf_classify_4x100_8_c : 6269.2
vvc_alf_classify_4x100_8_avx2 : 2470.2
vvc_alf_classify_4x100_10_c : 6335.5
vvc_alf_classify_4x100_10_avx2 : 2081.0
vvc_alf_classify_4x104_8_c : 6509.7
vvc_alf_classify_4x104_8_avx2 : 2468.5
vvc_alf_classify_4x104_10_c : 6553.0
vvc_alf_classify_4x104_10_avx2 : 2134.5
vvc_alf_classify_4x108_8_c : 6760.7
vvc_alf_classify_4x108_8_avx2 : 2661.2
vvc_alf_classify_4x108_10_c : 6983.2
vvc_alf_classify_4x108_10_avx2 : 2229.2
vvc_alf_classify_4x112_8_c : 6998.2
vvc_alf_classify_4x112_8_avx2 : 2650.7
vvc_alf_classify_4x112_10_c : 7041.5
vvc_alf_classify_4x112_10_avx2 : 2285.7
vvc_alf_classify_4x116_8_c : 7236.5
vvc_alf_classify_4x116_8_avx2 : 2833.2
vvc_alf_classify_4x116_10_c : 7470.0
vvc_alf_classify_4x116_10_avx2 : 2381.2
vvc_alf_classify_4x120_8_c : 7477.7
vvc_alf_classify_4x120_8_avx2 : 2827.2
vvc_alf_classify_4x120_10_c : 7524.0
vvc_alf_classify_4x120_10_avx2 : 2418.5
vvc_alf_classify_4x124_8_c : 7708.7
vvc_alf_classify_4x124_8_avx2 : 2947.2
vvc_alf_classify_4x124_10_c : 7818.7
vvc_alf_classify_4x124_10_avx2 : 2525.7
vvc_alf_classify_4x128_8_c : 7938.7
vvc_alf_classify_4x128_8_avx2 : 3009.7
vvc_alf_classify_4x128_10_c : 8016.2
vvc_alf_classify_4x128_10_avx2 : 2580.7
vvc_alf_classify_8x4_8_c : 722.5
vvc_alf_classify_8x4_8_avx2 : 211.7
vvc_alf_classify_8x4_10_c : 638.0
vvc_alf_classify_8x4_10_avx2 : 174.7
vvc_alf_classify_8x8_8_c : 1029.5
vvc_alf_classify_8x8_8_avx2 : 267.7
vvc_alf_classify_8x8_10_c : 1011.7
vvc_alf_classify_8x8_10_avx2 : 221.5
vvc_alf_classify_8x12_8_c : 1435.5
vvc_alf_classify_8x12_8_avx2 : 377.2
vvc_alf_classify_8x12_10_c : 1539.5
vvc_alf_classify_8x12_10_avx2 : 336.2
vvc_alf_classify_8x16_8_c : 3085.0
vvc_alf_classify_8x16_8_avx2 : 436.2
vvc_alf_classify_8x16_10_c : 1800.0
vvc_alf_classify_8x16_10_avx2 : 8534.5
vvc_alf_classify_8x20_8_c : 2208.0
vvc_alf_classify_8x20_8_avx2 : 560.5
vvc_alf_classify_8x20_10_c : 2137.5
vvc_alf_classify_8x20_10_avx2 : 480.7
vvc_alf_classify_8x24_8_c : 2542.0
vvc_alf_classify_8x24_8_avx2 : 620.7
vvc_alf_classify_8x24_10_c : 2485.5
vvc_alf_classify_8x24_10_avx2 : 542.2
vvc_alf_classify_8x28_8_c : 2895.0
vvc_alf_classify_8x28_8_avx2 : 751.7
vvc_alf_classify_8x28_10_c : 2887.5
vvc_alf_classify_8x28_10_avx2 : 634.2
vvc_alf_classify_8x32_8_c : 3297.0
vvc_alf_classify_8x32_8_avx2 : 903.5
vvc_alf_classify_8x32_10_c : 3277.0
vvc_alf_classify_8x32_10_avx2 : 702.2
vvc_alf_classify_8x36_8_c : 3656.7
vvc_alf_classify_8x36_8_avx2 : 919.5
vvc_alf_classify_8x36_10_c : 3621.7
vvc_alf_classify_8x36_10_avx2 : 789.0
vvc_alf_classify_8x40_8_c : 4050.0
vvc_alf_classify_8x40_8_avx2 : 985.0
vvc_alf_classify_8x40_10_c : 4025.5
vvc_alf_classify_8x40_10_avx2 : 833.7
vvc_alf_classify_8x44_8_c : 4403.0
vvc_alf_classify_8x44_8_avx2 : 1138.2
vvc_alf_classify_8x44_10_c : 4495.7
vvc_alf_classify_8x44_10_avx2 : 931.2
vvc_alf_classify_8x48_8_c : 4960.7
vvc_alf_classify_8x48_8_avx2 : 1199.7
vvc_alf_classify_8x48_10_c : 4784.2
vvc_alf_classify_8x48_10_avx2 : 1431.0
vvc_alf_classify_8x52_8_c : 11901.7
vvc_alf_classify_8x52_8_avx2 : 1286.5
vvc_alf_classify_8x52_10_c : 5744.5
vvc_alf_classify_8x52_10_avx2 : 1087.7
vvc_alf_classify_8x56_8_c : 5563.2
vvc_alf_classify_8x56_8_avx2 : 1356.5
vvc_alf_classify_8x56_10_c : 5486.5
vvc_alf_classify_8x56_10_avx2 : 1216.5
vvc_alf_classify_8x60_8_c : 6120.2
vvc_alf_classify_8x60_8_avx2 : 1477.0
vvc_alf_classify_8x60_10_c : 5869.2
vvc_alf_classify_8x60_10_avx2 : 1289.5
vvc_alf_classify_8x64_8_c : 6300.5
vvc_alf_classify_8x64_8_avx2 : 1533.7
vvc_alf_classify_8x64_10_c : 6255.7
vvc_alf_classify_8x64_10_avx2 : 1334.2
vvc_alf_classify_8x68_8_c : 6711.5
vvc_alf_classify_8x68_8_avx2 : 1658.7
vvc_alf_classify_8x68_10_c : 6625.0
vvc_alf_classify_8x68_10_avx2 : 1451.7
vvc_alf_classify_8x72_8_c : 7091.2
vvc_alf_classify_8x72_8_avx2 : 2300.0
vvc_alf_classify_8x72_10_c : 7002.7
vvc_alf_classify_8x72_10_avx2 : 1496.5
vvc_alf_classify_8x76_8_c : 7445.0
vvc_alf_classify_8x76_8_avx2 : 1883.0
vvc_alf_classify_8x76_10_c : 7394.5
vvc_alf_classify_8x76_10_avx2 : 1679.7
vvc_alf_classify_8x80_8_c : 8050.0
vvc_alf_classify_8x80_8_avx2 : 1889.7
vvc_alf_classify_8x80_10_c : 7767.5
vvc_alf_classify_8x80_10_avx2 : 1644.0
vvc_alf_classify_8x84_8_c : 8206.0
vvc_alf_classify_8x84_8_avx2 : 2147.0
vvc_alf_classify_8x84_10_c : 8361.0
vvc_alf_classify_8x84_10_avx2 : 1812.2
vvc_alf_classify_8x88_8_c : 8594.0
vvc_alf_classify_8x88_8_avx2 : 2140.0
vvc_alf_classify_8x88_10_c : 8497.2
vvc_alf_classify_8x88_10_avx2 : 1853.2
vvc_alf_classify_8x92_8_c : 8939.5
vvc_alf_classify_8x92_8_avx2 : 2265.7
vvc_alf_classify_8x92_10_c : 9144.7
vvc_alf_classify_8x92_10_avx2 : 2015.2
vvc_alf_classify_8x96_8_c : 9303.0
vvc_alf_classify_8x96_8_avx2 : 2329.0
vvc_alf_classify_8x96_10_c : 9262.0
vvc_alf_classify_8x96_10_avx2 : 2011.0
vvc_alf_classify_8x100_8_c : 9737.2
vvc_alf_classify_8x100_8_avx2 : 2511.5
vvc_alf_classify_8x100_10_c : 9603.0
vvc_alf_classify_8x100_10_avx2 : 2115.5
vvc_alf_classify_8x104_8_c : 10089.5
vvc_alf_classify_8x104_8_avx2 : 2506.2
vvc_alf_classify_8x104_10_c : 9994.7
vvc_alf_classify_8x104_10_avx2 : 2161.5
vvc_alf_classify_8x108_8_c : 10464.0
vvc_alf_classify_8x108_8_avx2 : 2700.2
vvc_alf_classify_8x108_10_c : 10395.5
vvc_alf_classify_8x108_10_avx2 : 2269.5
vvc_alf_classify_8x112_8_c : 10849.0
vvc_alf_classify_8x112_8_avx2 : 2691.0
vvc_alf_classify_8x112_10_c : 11047.7
vvc_alf_classify_8x112_10_avx2 : 2580.5
vvc_alf_classify_8x116_8_c : 11248.2
vvc_alf_classify_8x116_8_avx2 : 2876.7
vvc_alf_classify_8x116_10_c : 11139.5
vvc_alf_classify_8x116_10_avx2 : 2425.0
vvc_alf_classify_8x120_8_c : 25271.2
vvc_alf_classify_8x120_8_avx2 : 2874.2
vvc_alf_classify_8x120_10_c : 11568.2
vvc_alf_classify_8x120_10_avx2 : 2475.7
vvc_alf_classify_8x124_8_c : 12008.5
vvc_alf_classify_8x124_8_avx2 : 2991.0
vvc_alf_classify_8x124_10_c : 13275.5
vvc_alf_classify_8x124_10_avx2 : 2584.5
vvc_alf_classify_8x128_8_c : 12311.5
vvc_alf_classify_8x128_8_avx2 : 3048.5
vvc_alf_classify_8x128_10_c : 20640.0
vvc_alf_classify_8x128_10_avx2 : 2629.7
vvc_alf_classify_12x4_8_c : 962.5
vvc_alf_classify_12x4_8_avx2 : 208.2
vvc_alf_classify_12x4_10_c : 845.0
vvc_alf_classify_12x4_10_avx2 : 177.0
vvc_alf_classify_12x8_8_c : 1410.5
vvc_alf_classify_12x8_8_avx2 : 273.0
vvc_alf_classify_12x8_10_c : 1349.7
vvc_alf_classify_12x8_10_avx2 : 218.7
vvc_alf_classify_12x12_8_c : 1933.2
vvc_alf_classify_12x12_8_avx2 : 388.5
vvc_alf_classify_12x12_10_c : 1851.7
vvc_alf_classify_12x12_10_avx2 : 344.5
vvc_alf_classify_12x16_8_c : 2472.7
vvc_alf_classify_12x16_8_avx2 : 451.0
vvc_alf_classify_12x16_10_c : 2350.5
vvc_alf_classify_12x16_10_avx2 : 390.0
vvc_alf_classify_12x20_8_c : 2976.5
vvc_alf_classify_12x20_8_avx2 : 576.7
vvc_alf_classify_12x20_10_c : 2851.7
vvc_alf_classify_12x20_10_avx2 : 486.7
vvc_alf_classify_12x24_8_c : 3426.0
vvc_alf_classify_12x24_8_avx2 : 640.0
vvc_alf_classify_12x24_10_c : 3420.0
vvc_alf_classify_12x24_10_avx2 : 553.7
vvc_alf_classify_12x28_8_c : 3935.5
vvc_alf_classify_12x28_8_avx2 : 761.5
vvc_alf_classify_12x28_10_c : 3874.2
vvc_alf_classify_12x28_10_avx2 : 642.5
vvc_alf_classify_12x32_8_c : 4446.2
vvc_alf_classify_12x32_8_avx2 : 915.5
vvc_alf_classify_12x32_10_c : 4394.0
vvc_alf_classify_12x32_10_avx2 : 703.2
vvc_alf_classify_12x36_8_c : 4938.5
vvc_alf_classify_12x36_8_avx2 : 952.0
vvc_alf_classify_12x36_10_c : 4890.7
vvc_alf_classify_12x36_10_avx2 : 807.7
vvc_alf_classify_12x40_8_c : 5444.7
vvc_alf_classify_12x40_8_avx2 : 1011.0
vvc_alf_classify_12x40_10_c : 5397.7
vvc_alf_classify_12x40_10_avx2 : 851.7
vvc_alf_classify_12x44_8_c : 6510.2
vvc_alf_classify_12x44_8_avx2 : 1136.0
vvc_alf_classify_12x44_10_c : 6214.7
vvc_alf_classify_12x44_10_avx2 : 1040.0
vvc_alf_classify_12x48_8_c : 6486.7
vvc_alf_classify_12x48_8_avx2 : 1192.0
vvc_alf_classify_12x48_10_c : 6395.7
vvc_alf_classify_12x48_10_avx2 : 1422.7
vvc_alf_classify_12x52_8_c : 7058.5
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vvc_alf_classify_12x76_8_avx2 : 1927.0
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vvc_alf_classify_12x92_8_avx2 : 2309.7
vvc_alf_classify_12x92_10_c : 12249.0
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Segmentation Analytics : How to Leverage It on Your Site

27 octobre 2023, par Erin — Analytics Tips
The deeper you go with your customer analytics, the better your insights will be.

The result ? Your marketing performance soars to new heights.

Customer segmentation is one of the best ways businesses can align their marketing strategies with an effective output to generate better results. Marketers know that targeting the right people is one of the most important aspects of connecting with and converting web visitors into customers.

By diving into customer segmentation analytics, you’ll be able to transform your loosely defined and abstract audience into tangible, understandable segments, so you can serve them better.

In this guide, we’ll break down customer segmentation analytics, the different types, and how you can delve into these analytics on your website to grow your business.

What is customer segmentation ?

Before we dive into customer segmentation analytics, let’s take a step back and look at customer segmentation in general.

Customer segmentation is the process of dividing your customers up into different groups based on specific characteristics.

These groups could be based on demographics like age or location or behaviours like recent purchases or website visits.

By splitting your audience into different segments, your marketing team will be able to craft highly targeted and relevant marketing campaigns that are more likely to convert.

Additionally, customer segmentation allows businesses to gain new insights into their audience. For example, by diving deep into different segments, marketers can uncover pain points and desires, leading to increased conversion rates and return on investment.

But, to grasp the different customer segments, organisations need to know how to collect, digest and interpret the data for usable insights to improve their business. That’s where segmentation analytics comes in.

What is customer segmentation analytics ?

Customer segmentation analytics splits customers into different groups within your analytics software to create more detailed customer data and improve targeting.

With customer segmentation, you’re splitting your customers into different groups. With customer segmentation analytics, you’re doing this all within your analytics platform so you can understand them better.

One example of splitting your customers up is by country. For example, let’s say you have a global customer base. So, you go into your analytics software and find that 90% of your website visitors come from five countries : the UK, the US, Australia, Germany and Japan.

In this area, you could then create customer segmentation subsets based on these five countries. Moving forward, you could then hop into your analytics tool at any point in time and analyse the segments by country.

For example, if you wanted to see how well your recent marketing campaign impacted your Japanese customers, you could look at your Japanese subset within your analytics and dive into the data.

The primary goal of customer segmentation analytics is to gather actionable data points to give you an in-depth understanding of your customers. By gathering data on your different audience segments, you’ll discover insights on your customers that you can use to optimise your website, marketing campaigns, mobile apps, product offerings and overall customer experience.

Rather than lumping your entire customer base into a single mass, customer segmentation analytics allows you to meet even more specific and relevant needs and pain points of your customers to serve them better.

By allowing you to “zoom in” on your audience, segmentation analytics helps you offer more value to your customers, giving you a competitive advantage in the marketplace.

5 types of segmentation

There are dozens of different ways to split up your customers into segments. The one you choose depends on your goals and marketing efforts. Each type of segmentation offers a different view of your customers so you can better understand their specific needs to reach them more effectively.

While you can segment your customers in almost endless ways, five common types the majority fall under are :
Geographic

Another way to segment is by geography.

This is important because you could have drastically different interests, pain points and desires based on where you live.

If you’re running a global e-commerce website that sells a variety of clothing products, geographic segmentation can play a crucial role in optimising your website.

For instance, you may observe that a significant portion of your website visitors are from countries in the Southern Hemisphere, where it’s currently summer. On the other hand, visitors from the Northern Hemisphere are experiencing winter. Utilising this information, you can tailor your marketing strategy and website accordingly to increase sells.

Where someone comes from can significantly impact how they will respond to your messaging, brand and offer.

Geographic segmentation typically includes the following subtypes :

Cities (i.e., Austin, Paris, Berlin, etc.)
State (i.e., Massachusetts)
Country (i.e., Thailand)
Psychographic

Another key segmentation type of psychographic. This is where you split your customers into different groups based on their lifestyles.

Psychographic segmentation is a method of dividing your customers based on their habits, attitudes, values and opinions. You can unlock key emotional elements that impact your customers’ purchasing behaviours through this segmentation type.

Psychographic segmentation typically includes the following subtypes :

Values
Habits
Opinions
Behavioural

While psychographic segmentation looks at your customers’ overall lifestyle and habits, behavioural segmentation aims to dive into the specific individual actions they take daily, especially when interacting with your brand or your website.

Your customers won’t all interact with your brand the same way. They’ll act differently when interacting with your products and services for several reasons.

Behavioural segmentation can help reveal certain use cases, like why customers buy a certain product, how often they buy it, where they buy it and how they use it.

By unpacking these key details about your audience’s behaviour, you can optimise your campaigns and messaging to get the most out of your marketing efforts to reach new and existing customers.

Behavioural segmentation typically includes the following subtypes :

Interactions
Interests
Desires
Technographic

Another common segmentation type is technographic segmentation. As the name suggests, this technologically driven segment seeks to understand how your customers use technology.

While this is one of the newest segmentation types marketers use, it’s a powerful method to help you understand the types of tech your customers use, how often they use it and the specific ways they use it.

Technographic segmentation typically includes the following subtypes :

Smartphone type
Device type : smartphone, desktop, tablet
Apps
Video games
Demographic

The most common approach to segmentation is to split your customers up by demographics.

Demographic segmentation typically includes subtypes like language, job title, age or education.

This can be helpful for tailoring your content, products, and marketing efforts to specific audience segments. One way to capture this information is by using web analytics tools, where language is often available as a data point.

However, for accurate insights into other demographic segments like job titles, which may not be available (or accurate) in analytics tools, you may need to implement surveys or add fields to forms on your website to gather this specific information directly from your visitors.

How to build website segmentation analytics

With Matomo, you can create a variety of segments to divide your website visitors into different groups. Matomo’s Segments allows you to view segmentation analytics on subsets of your audience, like :

The device they used while visiting your site
What channel they entered your site from
What country they are located
Whether or not they visited a key page of your website
And more

While it’s important to collect general data on every visitor you have to your website, a key to website growth is understanding each type of visitor you have.

For example, here’s a screenshot of how you can segment all of your website’s visitors from New Zealand :
The criteria you use to define these segments are based on the data collected within your web analytics platform.

Here are some popular ways you can create some common themes on Matomo that can be used to create segments :

Visit based segments

Create segments in Matomo based on visitors’ patterns.

For example :

Do returning visitors show different traits than first-time visitors ?
Do people who arrive on your blog experience your website differently than those arriving on a landing page ?

This information can inform your content strategy, user interface design and marketing efforts.

Demographic segments

Create segments in Matomo based on people’s demographics.

For example :

User’s browser language
Location

This can enable you to tailor your approach to specific demographics, improving the performance of your marketing campaigns.

Technographic segments

Create segments in Matomo based on people’s technographics.

For example :

Web browser being used (i.e., Chrome, Safari, Firefox, etc.)
Device type (i.e., smartphone, tablet, desktop)

This can inform how to optimise your website based on users’ technology preferences, enhancing the effectiveness of your website.

Interaction based segments

Create segments in Matomo based on interactions.

For example :

Events (i.e., when someone clicks a specific URL on your website)
Goals (i.e., when someone stays on your site for a certain period)

Insights from this can empower you to fine-tune your content and user experience for increasing conversion rates.
Visitor profile view in Matomo with behavioural, location and technographic insights
Campaign-based segments

Create segments in Matomo based on campaigns.

For example :

Visitors arriving from specific traffic sources
Visitors arriving from specific advertising campaigns

With these insights, you can assess the performance of your marketing efforts, optimise your ad spend and make data-driven decisions to enhance your campaigns for better results.

Ecommerce segments

Create segments in Matomo based on ecommerce.

For example :

Visitors who purchased vs. those who didn’t
Visitors who purchased a specific product

This allows you to refine your website and marketing strategy for increased conversions and revenue.

Leverage Matomo for your segmentation analytics

By now, you can see the power of segmentation analytics and how they can be used to understand your customers and website visitors better. By breaking down your audience into groups, you’ll be able to gain insights into those segments to know how to serve them better with improved messaging and relevant products.

If you’re ready to begin using segmentation analytics on your website, try Matomo. Start your 21-day free trial now — no credit card required.

Matomo is an ideal choice for marketers looking for an easy-to-use, out-of-the-box web analytics solution that delivers accurate insights while keeping privacy and compliance at the forefront.
Benefits and Shortcomings of Multi-Touch Attribution

13 mars 2023, par Erin — Analytics Tips
Few sales happen instantly. Consumers take their time to discover, evaluate and become convinced to go with your offer.

Multi-channel attribution (also known as multi-touch attribution or MTA) helps businesses better understand which marketing tactics impact consumers’ decisions at different stages of their buying journey. Then double down on what’s working to secure more sales.

Unlike standard analytics, multi-channel modelling combines data from various channels to determine their cumulative and independent impact on your conversion rates.

The main benefit of multi-touch attribution is obvious : See top-performing channels, as well as those involved in assisted conversions. The drawback of multi-touch attribution : It comes with a more complex setup process.

If you’re on the fence about getting started with multi-touch attribution, here’s a summary of the main arguments for and against it.

What Are the Benefits of Multi-Touch Attribution ?

Remember an old parable of blind men and an elephant ?

Each one touched the elephant and drew conclusions about how it might look. The group ended up with different perceptions of the animal and thought the others were lying…until they decided to work together on establishing the truth.

Multi-channel analytics works in a similar way : It reconciles data from various channels and campaign types into one complete picture. So that you can get aligned on the efficacy of different campaign types and gain some other benefits too.
Better Understanding of Customer Journeys

On average, it takes 8 interactions with a prospect to generate a conversion. These interactions happen in three stages :

Awareness : You need to introduce your company to the target buyers and pique their interest in your solution (top-of-the-funnel).
Consideration : The next step is to channel this casual interest into deliberate research and evaluation of your offer (middle-of-the-funnel).
Decision : Finally, you need to get the buyer to commit to your offer and close the deal (bottom-of-the-funnel).

You can analyse funnels using various attribution models — last-click, fist-click, position-based attribution, etc. Each model, however, will spotlight the different element(s) of your sales funnel.

For example, a single-touch attribution model like last-click zooms in on the bottom-of-the-funnel stage. You can evaluate which channels (or on-site elements) sealed the deal for the prospect. For example, a site visitor arrived from an affiliate link and started a free trial. In this case, the affiliate (referral traffic) gets 100% credit for the conversion.

This measurement tactic, however, doesn’t show which channels brought the customer to the very bottom of your funnel. For instance, they may have interacted with a social media post, your landing pages or a banner ad before that.

Multi-touch attribution modelling takes funnel analysis a notch further. In this case, you map more steps in the customer journey — actions, events, and pages that triggered a visitor’s decision to convert — in your website analytics tool.
Then, select a multi-touch attribution model, which provides more backward visibility aka allows you to track more than one channel, preceding the conversion.

For example, a Position Based attribution model reports back on all interactions a site visitor had between their first visit and conversion.

A prospect first lands at your website via search results (Search traffic), which gets a 40% credit in this model. Two days later, the same person discovers a mention of your website on another blog and visits again (Referral traffic). This time, they save the page as a bookmark and revisit it again in two more days (Direct traffic). Each of these channels will get a 10% credit. A week later, the prospect lands again on your site via Twitter (Social) and makes a request for a demo. Social would then receive a 40% credit for this conversion. Last-click would have only credited social media and first-click — search engines.

The bottom line : Multi-channel attribution models show how different channels (and marketing tactics) contribute to conversions at different stages of the customer journey. Without it, you get an incomplete picture.

Improved Budget Allocation

Understanding causal relationships between marketing activities and conversion rates can help you optimise your budgets.

First-click/last-click attribution models emphasise the role of one channel. This can prompt you toward the wrong conclusions.

For instance, your Facebook ads campaigns do great according to a first-touch model. So you decide to increase the budget. What you might be missing though is that you could have an even higher conversion rate and revenue if you fix “funnel leaks” — address high drop-off rates during checkout, improve page layout and address other possible reasons for exiting the page.

Funnel reports at Matomo allow you to see how many people proceed to the next conversion stage and investigate why they drop off.

By knowing when and why people abandon their purchase journey, you can improve your marketing velocity (aka the speed of seeing the campaign results) and your marketing costs (aka the budgets you allocate toward different assets, touchpoints and campaign types).

Or as one of the godfathers of marketing technology, Dan McGaw, explained in a webinar :

“Once you have a multi-touch attribution model, you [can] actually know the return on ad spend on a per-campaign basis. Sometimes, you can get it down to keywords. Sometimes, you can get down to all kinds of other information, but you start to realise, “Oh, this campaign sucks. I should shut this off.” And then really, that’s what it’s about. It’s seeing those campaigns that suck and turning them off and then taking that budget and putting it into the campaigns that are working”.
More Accurate Measurements

The big boon of multi-channel marketing attribution is that you can zoom in on various elements of your funnel and gain granular data on the asset’s performance.

In other words : You get more accurate insights into the different elements involved in customer journeys. But for accurate analytics measurements, you must configure accurate tracking.

Define your objectives first : How do you want a multi-touch attribution tool to help you ? Multi-channel attribution analysis helps you answer important questions such as :

How many touchpoints are involved in the conversions ?
How long does it take for a lead to convert on average ?
When and where do different audience groups convert ?
What is your average win rate for different types of campaigns ?

Your objectives will dictate which multi-channel modelling approach will work best for your business — as well as the data you’ll need to collect.

At the highest level, you need to collect two data points :

Conversions : Desired actions from your prospects — a sale, a newsletter subscription, a form submission, etc. Record them as tracked Goals.
Touchpoints : Specific interactions between your brand and targets — specific page visits, referral traffic from a particular marketing channel, etc. Record them as tracked Events.

Your attribution modelling software will then establish correlation patterns between actions (conversions) and assets (touchpoints), which triggered them.

The accuracy of these measurements, however, will depend on the quality of data and the type of attribution modelling used.

Data quality stands for your ability to procure accurate, complete and comprehensive information from various touchpoints. For instance, some data won’t be available if the user rejected a cookie consent banner (unless you’re using a privacy-focused web analytics tool like Matomo).

Different attribution modelling techniques come with inherent shortcomings too as they don’t accurately represent the average sales cycle length or track visitor-level data, which allows you to understand which customer segments convert best.

Learn more about selecting the optimal multi-channel attribution model for your business.
What Are the Limitations of Multi-Touch Attribution ?

Overall, multi-touch attribution offers a more comprehensive view of the conversion paths. However, each attribution model (except for custom ones) comes with inherent assumptions about the contribution of different channels (e.g,. 25%-25%-25%-25% in linear attribution or 40%-10%-10%-40% in position-based attribution). These conversion credit allocations may not accurately represent the realities of your industry.

Also, most attribution models don’t reflect incremental revenue you gain from existing customers, which aren’t converting through analysed channels. For example, account upgrades to a higher tier, triggered via an in-app offer. Or warranty upsell, made via a marketing email.

In addition, you should keep in mind several other limitations of multi-touch attribution software.
Limited Marketing Mix Analysis

Multi-touch attribution tools work in conjunction with your website analytics app (as they draw most data from it). Because of that, such models inherit the same visibility into your marketing mix — a combo of tactics you use to influence consumer decisions.

Multi-touch attribution tools cannot evaluate the impact of :

Dark social channels
Word-of-mouth
Offline promotional events
TV or out-of-home ad campaigns

If you want to incorporate this data into your multi-attribution reporting, you’ll have to procure extra data from other systems — CRM, ad measurement partners, etc, — and create complex custom analytics models for its evaluation.
Time-Based Constraints

Most analytics apps provide a maximum 90-day lookback window for attribution. This can be short for companies with longer sales cycles.

Source : Marketing Charts

Marketing channels can be overlooked or underappreciated when your attribution window is too short. Because of that, you may curtail spending on brand awareness campaigns, which, in turn, will reduce the number of people entering the later stages of your funnel.

At the same time, many businesses would also want to track a look-forward window — the revenue you’ll get from one customer over their lifetime. In this case, not all tools may allow you to capture accurate information on repeat conversions — through re-purchases, account tier updates, add-ons, upsells, etc.

Again, to get an accurate picture you’ll need to understand how far into the future you should track conversions. Will you only record your first sales as a revenue number or monitor customer lifetime value (CLV) over 3, 6 or 12 months ?

The latter is more challenging to do. But CLV data can add another depth of dimension to your modelling accuracy. With Matomo, you set up this type of tracking by using our visitors’ tracking feature. We can help you track select visitors with known identifiers (e.g. name or email address) to discover their visiting patterns over time.

Limited Access to Raw Data

In web analytics, raw data stands for unprocessed website visitor information, stripped from any filters, segmentation or sampling applied.

Data sampling is a practice of analysing data subsets (instead of complete records) to extrapolate findings towards the entire data set. Google Analytics 4 applies data sampling once you hit over 500k sessions at the property level. So instead of accurate, real-life reporting, you receive approximations, generated by machine learning models. Data sampling is one of the main reasons behind Google Analytics’ accuracy issues.

In multi-channel attribution modelling, usage of sampled data creates further inconsistencies between the reports and the actual state of affairs. For instance, if your website generates 5 million page views, GA multi-touch analytical reports are based on the 500K sample size aka only 90% of the collected information. This hardly represents the real effect of all marketing channels and can lead to subpar decision-making.

With Matomo, the above is never an issue. We don’t apply data sampling to any websites (no matter the volume of traffic) and generate all the reports, including multi-channel attribution ones, based on 100% real user data.

AI Application

On the other hand, websites with smaller traffic volumes often have limited sampling datasets for building attribution models. Some tracking data may also be not available because the visitor rejected a cookie banner, for instance. On average, less than 50% of users in Australia, France, Germany, Denmark and the US among other countries always consent to all cookies.

To compensate for such scenarios, some multi-touch attribution solutions apply AI algorithms to “fill in the blanks”, which impacts the reporting accuracy. Once again, you get approximate data of what probably happened. However, Matomo is legally exempt from showing a cookie consent banner in most EU markets. Meaning you can collect 100% accurate data to make data-driven decisions.

Difficult Technical Implementation

Ever since attribution modelling got traction in digital marketing, more and more tools started to emerge.

Source : Markets and Markets

Most web analytics apps include multi-touch attribution reports. Then there are standalone multi-channel attribution platforms, offering extra features for conversion rate optimization, offline channel tracking, data-driven custom modelling, etc.

Most advanced solutions aren’t available out of the box. Instead, you have to install several applications, configure integrations with requested data sources, and then use the provided interfaces to code together custom data models. Such solutions are great if you have a technical marketer or a data science team. But a steep learning curve and high setup costs make them less attractive for smaller teams.

Conclusion

Multi-touch attribution modelling lifts the curtain in more steps, involved in various customer journeys. By understanding which touchpoints contribute to conversions, you can better plan your campaign types and budget allocations.

That said, to benefit from multi-touch attribution modelling, marketers also need to do the preliminary work : Determine the key goals, set up event and conversion tracking, and then — select the optimal attribution model type and tool.

Matomo combines simplicity with sophistication. We provide marketers with familiar, intuitive interfaces for setting up conversion tracking across the funnel. Then generate attribution reports, based on 100% accurate data (without any sampling or “guesstimation” applied). You can also get access to raw analytics data to create custom attribution models or plug it into another tool !

Start using accurate, easy-to-use multi-channel attribution with Matomo. Start your free 21-day trial now. No credit card requried.